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Objective: Functional data analysis (FDA) is a forecasting approach that, to date, has not been applied to obesity, and that may provide more accurate forecasting analysis to manage uncertainty in public health. This paper uses FDA to provide projections of Body Mass Index (BMI), overweight and obesity in an Australian population through to 2019., Methods: Data from the South Australian Monitoring and Surveillance System (January 2003 to December 2012, n=51,618 adults) were collected via telephone interview survey. FDA was conducted in four steps: 1) age-gender specific BMIs for each year were smoothed using a weighted regression; 2) the functional principal components decomposition was applied to estimate the basis functions; 3) an exponential smoothing state space model was used for forecasting the coefficient series; and 4) forecast coefficients were combined with the basis function., Results: The forecast models suggest that between 2012 and 2019 average BMI will increase from 27.2 kg/m(2) to 28.0 kg/m(2) in males and 26.4 kg/m(2) to 27.6 kg/m(2) in females. The prevalence of obesity is forecast to increase by 6-7 percentage points by 2019 (to 28.7% in males and 29.2% in females)., Conclusions: Projections identify age-gender groups at greatest risk of obesity over time. The novel approach will be useful to facilitate more accurate planning and policy development., (© 2015 Public Health Association of Australia.)

ObjectiveThe present study aimed to define the complexity of the relationships between the family environment, health behaviours and obesity. A conceptual model that quantifies the relationships and interactions between parent factors, family environment, and certain aspects of children's behaviour and weight status is presented.DesignExploratory structural equation modelling was used to quantitatively model the relationships between parent, child and family environmental factors.SettingAdelaide, South Australia.SubjectsFamilies (n 157) with children aged 5–10 years completed self-reported questionnaires, providing data on parents’ knowledge, diet quality and activity habits; child feeding and general parenting styles; and the food and physical activity environments. Outcome variables included children's fruit and vegetable intake, activity and sedentary habits and weight status.ResultsThe proposed model was an acceptable fit (normed fit index = 0·457; comparative fit index = 0·746; root-mean-squared error associated = 0·044). Parents’ BMI (β = 0·32) and nutrition and physical activity knowledge (β = 0·17) had the strongest direct associations with children's BMI Z-score. Parents’ dietary intake and energy expenditure behaviours were indirectly associated with children's behaviour through the creation of the home environment. The physical activity and food environments were associated with children's sedentary (β = −0·44) and activity habits (β = 0·29), and fruit and vegetable intake (β = 0·47), respectively.ConclusionsA conceptual model that quantifies the complex network of family environment factors influencing children's behaviour and weight status is presented. The model provides a basis for future research on larger representative samples with a view to guiding obesity prevention interventions. [ABSTRACT FROM PUBLISHER]